Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-22T14:50:28.611Z Has data issue: false hasContentIssue false
  • English
  • Français

Autonomic complexity and emotion (dys-)regulation in early childhood across high- and low-risk contexts

Published online by Cambridge University Press:  10 July 2019

Daniel Berry ,
Alyssa R. Palmer ,
Rebecca Distefano  and
Ann S. Masten
Daniel Berry*
Affiliation:
Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
Alyssa R. Palmer
Affiliation:
Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
Rebecca Distefano
Affiliation:
Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
Ann S. Masten
Affiliation:
Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
*
Author for Correspondence: Daniel Berry, Institute of Child Development, University of Minnesota, 51 East River Parkway, Minneapolis, MN 55455; E-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Developing the ability to regulate one's emotions in accordance with contextual demands (i.e., emotion regulation) is a central developmental task of early childhood. These processes are supported by the engagement of the autonomic nervous system (ANS), a physiological hub of a vast network tasked with dynamically integrating real-time experiential inputs with internal motivational and goal states. To date, much of what is known about the ANS and emotion regulation has been based on measures of respiratory sinus arrhythmia, a cardiac indicator of parasympathetic activity. In the present study, we draw from dynamical systems models to introduce two nonlinear indices of cardiac complexity (fractality and sample entropy) as potential indicators of these broader ANS dynamics. Using data from a stratified sample of preschoolers living in high- (i.e., emergency homeless shelter) and low-risk contexts (N = 115), we show that, in conjunction with respiratory sinus arrhythmia, these nonlinear indices may help to clarify important differences in the behavioral manifestations of emotion regulation. In particular, our results suggest that cardiac complexity may be especially useful for discerning active, effortful emotion regulation from less effortful regulation and dysregulation.

Keywords

autonomic nervous systemdynamical systemsemotion regulationentropyfractalrespiratory sinus arrhythmia
Type
Special Issue Articles
Information
Development and Psychopathology , Volume 31 , Special Issue 3: Emotion Dysregulation and Emerging Psychopathology , August 2019 , pp. 1173 - 1190
Copyright
Copyright © Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aldao, A., Gee, D. G., De Los Reyes, A., & Seager, I. (2016). Emotion regulation as a transdiagnostic factor in the development of internalizing and externalizing psychopathology: Current and future directions. Development and Psychopathology, 28, 927–946. doi:10.1017/S0954579416000638Google Scholar
Angelini, L., Maestri, R., Marinazzo, D., Nitti, L., Pellicoro, M., Pinna, G. D., … Tupputi, S. A. (2007). Multiscale analysis of short term heart beat interval, arterial blood pressure, and instantaneous lung volume time series. Artificial Intelligence in Medicine, 41, 237–250. doi:10.1016/j.artmed.2007.07.012Google Scholar
Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using M plus. Structural Equation Modeling, 21, 329–341. doi:10.1080/10705511.2014.915181Google Scholar
Bär, K. J., Boettger, M. K., Koschke, M., Schulz, S., Chokka, P., Yeragani, V. K., & Voss, A. (2007). Non-linear complexity measures of heart rate variability in acute schizophrenia. Clinical Neurophysiology, 118, 2009–2015. doi:10.1016/j.clinph.2007.06.012Google Scholar
Barbieri, R., Scilingo, E. P., & Valenza, G. (Eds.) (2017). Complexity and nonlinearity in cardiovascular signals. Cham, Switzerland: Springer.Google Scholar
Barrett, L. F., Wilson-Mendenhall, C. D., & Barsalou, L. W. (2013). A psychological construction account of emotion regulation and dysregulation: The role of situated conceptualizations. In Gross, J. J. (Ed.), Handbook of emotion regulation (2nd ed., pp. 447–465). New York: Guilford Press.Google Scholar
Bassuk, E. L., Richard, M. K., & Tsertsvadze, A. (2015). The prevalence of mental illness in homeless children: A systematic review and meta-analysis. Journal of the American Academy of Child & Adolescent Psychiatry, 54, 86–96. doi:10.1016/j.jaac.2014.11.008Google Scholar
Beauchaine, T. (2001). Vagal tone, development, and Gray's motivational theory: Toward an integrated model of autonomic nervous system functioning in psychopathology. Development and Psychopathology, 13, 183–214. doi:10.1017/s0954579401002012Google Scholar
Beauchaine, T. P. (2012). Physiological markers of emotion and behavior dysregulation in externalizing psychopathology. Monographs of the Society for Research in Child Development, 77, 79–86. doi:10.1111/j.1540-5834.2011.00665.xGoogle Scholar
Beauchaine, T. P. (2015a). Respiratory sinus arrhythmia: A transdiagnostic biomarker of emotion dysregulation and psychopathology. Current Opinion in Psychology, 3, 43–47. doi:10.1016/j.copsyc.2015.01.017Google Scholar
Beauchaine, T. P. (2015b). Future directions in emotion dysregulation and youth psychopathology. Journal of Clinical Child and Adolescent Psychology, 44, 875–896. doi:10.1080/15374416.2015.1038827Google Scholar
Beauchaine, T. P., Bell, Z., Knapton, E., McDonough-Caplan, H., Shader, T., & Zisner, A. (2019). Respiratory sinus arrhythmia reactivity across empirically based structural dimensions of psychopathology: A meta-analysis. Psychophysiology, e13329. doi:10.1111/psyp.13329Google Scholar
Beauchaine, T. P., Gatzke-Kopp, L., & Mead, H. K. (2007). Polyvagal theory and developmental psychopathology: Emotion dysregulation and conduct problems from preschool to adolescence. Biological Psychology, 74, 174–184. doi:10.1016/j.biopsycho.2005.08.008Google Scholar
Beauchaine, T. P., & Thayer, J. F. (2015). Heart rate variability as a transdiagnostic biomarker of psychopathology. International Journal of Psychophysiology, 98, 338–350. doi:10.1016/j.ijpsycho.2015.08.004Google Scholar
Beckers, F., Verheyden, B., & Aubert, A. E. (2006). Ageing and non-linear heart rate control in a healthy population. American Journal of Physiology—Heart and Circulatory Physiology, 290, H2560–H2570. doi:10.1152/ajpheart.00903.2005Google Scholar
Benarroch, E. E. (1993). The central autonomic network: Functional organization, dysfunction, and perspective. Mayo Clinic Proceedings, 68, 988–1001. doi:10.1016/s0025-6196(12)62272-1Google Scholar
Berry, D., Blair, C., Willoughby, M., Granger, D. A., Mills-Koonce, W. R., & Family Life Project Key Investigators. (2017). Maternal sensitivity and adrenocortical functioning across infancy and toddlerhood: Physiological adaptation to context? Development and Psychopathology, 29, 303–317. doi:10.1017/s0954579416000158Google Scholar
Berry, D., & Stallworthy, I. (2018). Complex heart, complex mind: The fractal nature of HRV in cognitive control. Poster presented at the International Society for Developmental Psychobiology Annual Meeting, San Diego, CA.Google Scholar
Blair, C., Berry, D. J., & Family Life Project Key Investigators. (2017). Moderate within-person variability in cortisol is related to executive function in early childhood. Psychoneuroendocrinology, 81, 88–95. doi:10.1016/j.psyneuen.2017.03.026Google Scholar
Blair, C., & Raver, C. C. (2012). Child development in the context of adversity: Experiential canalization of brain and behavior. American Psychologist, 67, 309–318. doi:10.1037/a0027493Google Scholar
Blair, C., & Raver, C. C. (2015). School readiness and self-regulation: A developmental psychobiological approach. Annual Review of Psychology, 66, 711–731. doi:10.1146/annurev-psych-010814-015221Google Scholar
Bornas, X., Balle, M., de la Torre-Luque, A., Fiol-Veny, A., & Llabrés, J. (2015). Ecological assessment of heart rate complexity: Differences between high- and low-anxious adolescents. International Journal of Psychophysiology, 98, 112–118. doi:10.1016/j.ijpsycho.2015.07.007Google Scholar
Calkins, S. D., Graziano, P. A., & Keane, S. P. (2007). Cardiac vagal regulation differentiates among children at risk for behavior problems. Biological Psychology, 74, 144–153. doi:10.1016/j.biopsycho.2006.09.005Google Scholar
Calkins, S. D., Smith, C. L., Gill, K. L., & Johnson, M. C. (1998). Maternal interactive style across contexts: Relations to emotional, behavioral and physiological regulation during toddlerhood. Social Development, 7, 350–369. doi:10.1111/1467-9507.00072Google Scholar
Chang, J. S., Yoo, C. S., Yi, S. H., Hong, K. H., Oh, H. S., Hwang, J. Y., … Kim, Y. S. (2009). Differential pattern of heart rate variability in patients with schizophrenia. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 33, 991–995. doi:10.1016/j.pnpbp.2009.05.004Google Scholar
Cicchetti, D., Ackerman, B. P., & Izard, C. E. (1995). Emotions and emotion regulation in developmental psychopathology. Development and Psychopathology, 7, 1–10. doi:10.1017/s0954579400006301Google Scholar
Coey, C. A., Wallot, S., Richardson, M. J., & Van Orden, G. (2012). On the structure of measurement noise in eye-tracking. Journal of Eye Movement Research, 5, 1–10.Google Scholar
Cole, P. M., Bendezú, J. J., Ram, N., & Chow, S. M. (2017). Dynamical systems modeling of early childhood self-regulation. Emotion, 17, 684–699. doi:10.1037/emo0000268Google Scholar
Cole, P. M., Hall, S. E., & Hajal, N. J. (2013). Emotion dysregulation as a risk factor for psychopathology. In Beauchaine, T. P. & Hinshaw, S. P. (Eds.), Child and adolescent psychopathology (pp. 341–373). Hoboken, NJ: Wiley.Google Scholar
Cole, P. M., Martin, S. E., & Dennis, T. A. (2004). Emotion regulation as a scientific construct: Methodological challenges and directions for child development research. Child Development, 75, 317–333. doi:10.1111/j.1467-8624.2004.00673.xGoogle Scholar
Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. Hoboken, NJ: Wiley.Google Scholar
Compas, B. E., Jaser, S. S., Bettis, A. H., Watson, K. H., Gruhn, M. A., Dunbar, J. P., … Thigpen, J. C. (2017). Coping, emotion regulation, and psychopathology in childhood and adolescence: A meta-analysis and narrative review. Psychological Bulletin, 143, 939–991. doi:10.1037/bul0000110Google Scholar
Costa, M., Goldberger, A. L., & Peng, C. K. (2002). Multiscale entropy analysis of complex physiologic time series. Physical Review Letters, 89, 068102-1–068102-4. doi:10.1103/physrevlett.89.068102Google Scholar
Cutuli, J. J., & Herbers, J. E. (2014). Promoting resilience for children who experience family homelessness: Opportunities to encourage developmental competence. Cityscape, 16, 113–140.Google Scholar
de la Torre-Luque, A., Bornas, X., Balle, M., & Fiol-Veny, A. (2016). Complexity and nonlinear biomarkers in emotional disorders: A meta-analytic study. Neuroscience & Biobehavioral Reviews, 68, 410–422. doi:10.1016/j.neubiorev.2016.05.023Google Scholar
Del Giudice, M., Ellis, B. J., & Shirtcliff, E. A. (2011). The adaptive calibration model of stress responsivity. Neuroscience & Biobehavioral Reviews, 35, 1562–1592. doi:10.1016/j.neubiorev.2010.11.007Google Scholar
Eisenberg, N., Spinrad, T. L., & Eggum, N. D. (2010). Emotion-related self-regulation and its relation to children's maladjustment. Annual Review of Clinical Psychology, 6, 495–525. doi:10.1146/annurev.clinpsy.121208.131208Google Scholar
Feldman, R. (2009). The development of regulatory functions from birth to 5 years: Insights from premature infants. Child Development, 80, 544–561. doi:10.1111/j.1467-8624.2009.01278.xGoogle Scholar
Ferrario, M., Signorini, M. G., & Magenes, G. (2009). Complexity analysis of the fetal heart rate variability: Early identification of severe intrauterine growth-restricted fetuses. Medical & Biological Engineering & Computing, 47, 911–919. doi:10.1007/s11517-009-0502-8Google Scholar
Fiskum, C., Andersen, T. G., Bornas, X., Aslaksen, P. M., Flaten, M. A., & Jacobsen, K. (2018). Nonlinear heart rate variability as a discriminator of internalizing psychopathology and negative affect in children with internalizing problems and healthy controls. Frontiers in Physiology, 9, 561. doi:10.3389/fphys.2018.00561Google Scholar
Forbes, E. E., Fox, N. A., Cohn, J. F., Galles, S. F., & Kovacs, M. (2006). Children's affect regulation during a disappointment: Psychophysiological responses and relation to parent history of depression. Biological Psychology, 71, 264–277. doi:10.1016/j.biopsycho.2005.05.004Google Scholar
Gates, K. M., Gatzke-Kopp, L. M., Sandsten, M., & Blandon, A. Y. (2015). Estimating time-varying RSA to examine psychophysiological linkage of marital dyads. Psychophysiology, 52, 1059–1065. doi:10.1111/psyp.12428Google Scholar
Goldsmith, H. H., Reilly, J., Lemery, K. S., Longley, S., & Prescott, A. (1999). The Laboratory Assessment Battery: Preschool version (LAB-TAB). Madison, WI: University of Wisconsin Press.Google Scholar
Graff, B., Graff, G., & Kaczkowska, A. (2012). Entropy measures of heart rate variability for short ECG datasets in patients with congestive heart failure. Acta Physica Polonica B Proceedings Supplement, 5, 153–158. doi:10.5506/APhysPolBSupp.5.153Google Scholar
Graham, J. W. (2003). Adding missing-data-relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10, 80–100. doi:10.1207/s15328007sem1001_4Google Scholar
Graziano, P., & Derefinko, K. (2013). Cardiac vagal control and children's adaptive functioning: A meta-analysis. Biological Psychology, 94, 22–37. doi:10.1016/j.biopsycho.2013.04.011Google Scholar
Griffin, M. P., Lake, D. E., & Moorman, J. R. (2005). Heart rate characteristics and laboratory tests in neonatal sepsis. Pediatrics—English Edition, 115, 937–941. doi:10.1542/peds.2004-1393Google Scholar
Gross, J. J., & Jazaieri, H. (2014). Emotion, emotion regulation, and psychopathology: An affective science perspective. Clinical Psychological Science, 2, 387–401. doi:10.1177/2167702614536164Google Scholar
Gross, J. J., & Thompson, R. A. (2007). Emotion regulation: Conceptual foundations. In Gross, J. J. (Ed.), Handbook of emotion regulation (pp. 3–24). New York: Guilford Press.Google Scholar
Hastings, P. S., Miller, J. G., Kahle, S., & Zahn-Waxler, C. (2014). The neurobiological bases of empathic concern for others. In Killen, M. & Smetana, J. G. (Eds.), Handbook of moral development (pp. 411–434). New York: Psychology Press.Google Scholar
Heneghan, C., & McDarby, G. (2000). Establishing the relation between detrended fluctuation analysis and power spectral density analysis for stochastic processes. Physical review E, 62, 6103–6110. doi:10.1103/PhysRevE.62.6103Google Scholar
Holmes, A., & Wellman, C. L. (2009). Stress-induced prefrontal reorganization and executive dysfunction in rodents. Neuroscience & Biobehavioral Reviews, 33, 773–783. doi:10.1016/j.neubiorev.2008.11.005Google Scholar
Ihlen, E. A. F. E. (2012). Introduction to multifractal detrended fluctuation analysis in Matlab. Frontiers in Physiology, 3, 141. doi:10.3389/fphys.2012.00141Google Scholar
Jennings, J. R., Kamarck, T., Stewart, C., Eddy, M., & Johnson, P. (1992). Alternate cardiovascular baseline assessment techniques: Vanilla or resting baseline. Psychophysiology, 29, 742–750. doi:10.1111/j.1469-8986.1992.tb02052.xGoogle Scholar
Jones, S. M., Bailey, R., Barnes, S. P., & Partee, A. (2016). Executive Function Mapping Project Executive Summary: Untangling the Terms and Skills Related to Executive Function and Self-Regulation in Early Childhood. OPRE Report # 2016-88. Washington, DC: US Department of Health and Human Services, Administration for Children and Families, Office of Planning, Research and Evaluation.Google Scholar
Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and Its Applications, 316, 87–114. doi:10.1016/s0378-4371(02)01383-3Google Scholar
Karssen, A. M., Her, S., Li, J. Z., Patel, P. D., Meng, F., Bunney, W. E. Jr., … Schatzberg, A. F. (2007). Stress-induced changes in primate prefrontal profiles of gene expression. Molecular Psychiatry, 12, 1089. doi:10.1038/sj.mp.4002095Google Scholar
Kelty-Stephen, D. G., Stirling, L. A., & Lipsitz, L. A. (2016). Multifractal temporal correlations in circle-tracing behaviors are associated with the executive function of rule-switching assessed by the Trail Making Test. Psychological Assessment, 28, 171. doi:10.1037/pas0000177Google Scholar
Koenig, J., Kemp, A. H., Beauchaine, T. P., Thayer, J. F., & Kaess, M. (2016). Depression and resting state heart rate variability in children and adolescents—A systematic review and meta-analysis. Clinical Psychology Review, 46, 136–150. doi:10.1016/j.cpr.2016.04.013Google Scholar
Kolmogorov, A. N. (1958). New metric invariant of transitive dynamical systems and endomorphisms of Lebesgue spaces. Doklady of Russian Academy of Sciences, 119, 861–864.Google Scholar
Labella, M. H., Narayan, A. J., & Masten, A. S. (2016). Emotional climate in families experiencing homelessness: Associations with child affect and socioemotional adjustment in school. Social Development, 25, 304–321. doi:10.1111/sode.12154Google Scholar
Lake, D. E., Richman, J. S., Griffin, M. P., & Moorman, J. R. (2002). Sample entropy analysis of neonatal heart rate variability. American Journal of Physiology—Regulatory, Integrative and Comparative Physiology, 283, R789–R797. doi:10.1152/ajpregu.00069.2002Google Scholar
Lewis, M., Hitchcock, D. F., & Sullivan, M. W. (2004). Physiological and emotional reactivity to learning and frustration. Infancy, 6, 121–143. doi:10.1207/s15327078in0601_6Google Scholar
Lipsitz, L. A. (2002). Dynamics of stability: The physiologic basis of functional health and frailty. Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 57, B115–B125. doi:10.1093/gerona/57.3.b115Google Scholar
Liston, C., Miller, M. M., Goldwater, D. S., Radley, J. J., Rocher, A. B., Hof, P. R., … McEwen, B. S. (2006). Stress-induced alterations in prefrontal cortical dendritic morphology predict selective impairments in perceptual attentional set-shifting. Journal of Neuroscience, 26, 7870–7874. doi:10.1523/jneurosci.1184-06.2006Google Scholar
Lyons, D. M., Parker, K. J., & Schatzberg, A. F. (2010). Animal models of early life stress: Implications for understanding resilience. Developmental Psychobiology, 52, 402–410. doi:10.1002/dev.20429Google Scholar
Mandelbrot, B. B. (1977). Fractals: Form, chance, and dimension (Vol. 706). San Francisco: Freeman.Google Scholar
Masi, C. M., Hawkley, L. C., Rickett, E. M., & Cacioppo, J. T. (2007). Respiratory sinus arrhythmia and diseases of aging: Obesity, diabetes mellitus, and hypertension. Biological Psychology, 74, 212–223. doi:10.1016/j.biopsycho.2006.07.006Google Scholar
Masten, A. S., Fiat, A. E., Labella, M. H., & Strack, R. A. (2015). Educating homeless and highly mobile students: Implications of research on risk and resilience. School Psychology Review, 44, 315–330. doi:10.17105/spr-15-0068.1Google Scholar
Masyn, K. E. (2013). Latent class analysis and finite mixture modeling. In Nathan, P. E. (Ed.), The Oxford handbook of quantitative methods (pp. 551–611). New York: Oxford University Press.Google Scholar
McArdle, J. J., & Hamagami, F. (2001). Advanced studies of individual differences: Linear dynamic models for longitudinal data analysis. In Marcoulides, G. A. & Schumacker, R. E. (Eds.), New developments and techniques in structural equation modeling (pp. 223–266). New York: Psychology Press.Google Scholar
Molenaar, P. C. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement, 2, 201–218. doi:10.1207/s15366359mea0204_1Google Scholar
Muthén, L. K., & Muthén, B. O. (1998–2017). Mplus user's guide (8th ed.). Los Angeles: Author.Google Scholar
Nardelli, M., Lanata, A., Bertschy, G., Scilingo, E. P., & Valenza, G. (2017). Heartbeat complexity modulation in bipolar disorder during daytime and nighttime. Scientific Reports, 7, 1–11. doi:10.1038/s41598-017-18036-zGoogle Scholar
Nelson-Le Gall, S. (1981). Help-seeking: An understudied problem-solving skill in children. Developmental Review, 1, 224–246. doi:10.1016/0273-2297(81)90019-8Google Scholar
Nigg, J. T. (2017). Annual Research Review: On the relations among self-regulation, self-control, executive functioning, effortful control, cognitive control, impulsivity, risk-taking, and inhibition for developmental psychopathology. Journal of Child Psychology and Psychiatry, 58, 361–383. doi:10.1111/jcpp.12675Google Scholar
Obradović, J., Bush, N. R., Stamperdahl, J., Adler, N. E., & Boyce, W. T. (2010). Biological sensitivity to context: The interactive effects of stress reactivity and family adversity on socioemotional behavior and school readiness. Child Development, 81, 270–289. doi:10.1111/j.1467-8624.2009.01394.xGoogle Scholar
Palmer, A. R., Berry, D., & Leneman, K. (2018, October). Validation of using FIRSTBEAT BODYGUARD2 to collect interbeat interval data with preschool-aged children. Poster and flash talk presented at the Society for Psychophysiological Research Annual Meeting, Quebec, Canada.Google Scholar
Patel, P. D., Katz, M., Karssen, A. M., & Lyons, D. M. (2008). Stress-induced changes in corticosteroid receptor expression in primate hippocampus and prefrontal cortex. Psychoneuroendocrinology, 33, 360–367. doi:10.1016/j.psyneuen.2007.12.003Google Scholar
Patriquin, M. A., Lorenzi, J., Scarpa, A., & Bell, M. A. (2014). Developmental trajectories of respiratory sinus arrhythmia: Associations with social responsiveness. Developmental Psychobiology, 56, 317–326. doi:10.1002/dev.21100Google Scholar
Patriquin, M. A., Lorenzi, J., Scarpa, A., Calkins, S. D., & Bell, M. A. (2015). Broad implications for respiratory sinus arrhythmia development: Associations with childhood symptoms of psychopathology in a community sample. Developmental Psychobiology, 57, 120–130. doi:10.1002/dev.21269Google Scholar
Peng, C. K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos, 5, 82–87.Google Scholar
Perry, N. B., Calkins, S. D., Dollar, J. M., Keane, S. P., & Shanahan, L. (2018). Self-regulation as a predictor of patterns of change in externalizing behaviors from infancy to adolescence. Development and Psychopathology, 30, 497–510. doi:10.1017/s0954579417000992Google Scholar
Perry, N. B., Calkins, S. D., Nelson, J. A., Leerkes, E. M., & Marcovitch, S. (2012). Mothers' responses to children's negative emotions and child emotion regulation: The moderating role of vagal suppression. Developmental Psychobiology, 54, 503–513. doi:10.1002/dev.20608Google Scholar
Pikkujämsä, S. M., Mäkikallio, T. H., Sourander, L. B., Räihä, I. J., Puukka, P., Skyttä, J., … Huikuri, H. V. (1999). Cardiac interbeat interval dynamics from childhood to senescence: Comparison of conventional and new measures based on fractals and chaos theory. Circulation, 100, 393–399. doi:10.1161/01.cir.100.4.393Google Scholar
Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88, 2297–2301. doi:10.1073/pnas.88.6.2297Google Scholar
Poon, C. S., & Merrill, C. K. (1997 ). Decrease of cardiac chaos in congestive heart failure. Nature, 389, 492–495. doi:10.1038/39043Google Scholar
Porges, S. W. (1995). Orienting in a defensive world: Mammalian modifications of our evolutionary heritage. A polyvagal theory. Psychophysiology, 32, 301–318. doi:10.1111/j.1469-8986.1995.tb01213.xGoogle Scholar
Porges, S. W. (2007). The polyvagal perspective. Biological Psychology, 74, 116–143. doi:10.1016/j.biopsycho.2006.06.009Google Scholar
Ramshur, J. T. (2010). Design, evaluation, and application of heart rate variability analysis software (HRVA). Unpublished doctoral dissertation, University of Memphis.Google Scholar
Ramsook, K. A., Cole, P. M., & Fields-Olivieri, M. A. (2018). What emotion dysregulation looks like. In Beauchaine, T. P., & Crowell, S. E. (Eds.), The Oxford handbook of emotion dysregulation. Oxford: Oxford University Press.Google Scholar
Reijntjes, A., Stegge, H., Terwogt, M. M., Kamphuis, J. H., & Telch, M. J. (2006). Emotion regulation and its effects on mood improvement in response to an in vivo peer rejection challenge. Emotion, 6, 543–552. doi:10.1037/1528-3542.6.4.543Google Scholar
Richards, J. E. (1987). Infant visual sustained attention and respiratory sinus arrhythmia. Child Development, 58, 488–496. doi:10.2307/1130525Google Scholar
Richman, J. S., Lake, D. E., & Moorman, J. R. (2004). Sample entropy. In Methods in enzymology (Vol. 384, pp. 172–184). New York: Academic Press.Google Scholar
Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology—Heart and Circulatory Physiology, 278, H2039–H2049. doi:10.1152/ajpheart.2000.278.6.h2039Google Scholar
Rodríguez-Liñares, L., Méndez, A. J., Lado, M. J., Olivieri, D. N., Vila, X. A., & Gómez-Conde, I. (2011). An open source tool for heart rate variability spectral analysis. Computer Methods and Programs in Biomedicine, 103, 39–50.Google Scholar
Rothbart, M. K., & Sheese, B. E. (2007). Temperament and emotion regulation. In Gross, J. J. (Ed.), Handbook of emotion regulation (pp. 331–350). New York: Guilford Press.Google Scholar
Sassi, R., Cerutti, S., Lombardi, F., Malik, M., Huikuri, H. V., Peng, C. K., … Lip, G. Y. (2015). Advances in heart rate variability signal analysis: Joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. Europace, 17, 1341–1353. doi:10.1093/europace/euv015Google Scholar
Schmitz, J., Krämer, M., Tuschen-Caffier, B., Heinrichs, N., & Blechert, J. (2011). Restricted autonomic flexibility in children with social phobia. Journal of Child Psychology and Psychiatry, 52, 1203–1211. doi:10.1111/j.1469-7610.2011.02417.xGoogle Scholar
Seoane-Collazo, P., Fernø, J., Gonzalez, F., Diéguez, C., Leis, R., Nogueiras, R., & López, M. (2015). Hypothalamic-autonomic control of energy homeostasis. Endocrine, 50, 276–291. doi:10.1007/s12020-015-0658-yGoogle Scholar
Shader, T. M., Gatzke-Kopp, L. M., Crowell, S. E., Reid, M. J., Thayer, J. F., Vasey, M. W., … Beauchaine, T. P. (2018). Quantifying respiratory sinus arrhythmia: Effects of misspecifying breathing frequencies across development. Development and Psychopathology, 30, 351–366. doi:10.1017/S0954579417000669Google Scholar
Shahrestani, S., Stewart, E. M., Quintana, D. S., Hickie, I. B., & Guastella, A. J. (2014). Heart rate variability during social interactions in children with and without psychopathology: A meta-analysis. Journal of Child Psychology and Psychiatry, 55, 981–989. doi:10.1111/jcpp.12226Google Scholar
Shannon, C. E. (1948). A mathematical theory of communication. Bell system technical Journal, 27, 379–423.Google Scholar
Sheppes, G., Suri, G., & Gross, J. J. (2015). Emotion regulation and psychopathology. Annual Review of Clinical Psychology, 11, 379–405. doi:10.1146/annurev-clinpsy-032814-112739Google Scholar
Smith, R., Thayer, J. F., Khalsa, S. S., & Lane, R. D. (2017). The hierarchical basis of neurovisceral integration. Neuroscience & Biobehavioral Reviews, 75, 274–296. doi:10.1016/j.neubiorev.2017.02.003Google Scholar
Stansbury, K., & Sigman, M. (2000). Responses of preschoolers in two frustrating episodes: Emergence of complex strategies for emotion regulation. Journal of Genetic Psychology, 161, 182–202. doi:10.1080/00221320009596705Google Scholar
Stifter, C. A., Dollar, J. M., & Cipriano, E. A. (2011). Temperament and emotion regulation: The role of autonomic nervous system reactivity. Developmental Psychobiology, 53, 266–279. doi:10.1002/dev.20519Google Scholar
Suess, P. E., Porges, S. W., & Plude, D. J. (1994). Cardiac vagal tone and sustained attention in school-age children. Psychophysiology, 31, 17–22. doi:10.1111/j.1469-8986.1994.tb01020.xGoogle Scholar
Tarvainen, M. P., Niskanen, J. P., Lipponen, J. A., Ranta-Aho, P. O., & Karjalainen, P. A. (2014). Kubios HRV—Heart rate variability analysis software. Computer Methods and Programs in Biomedicine, 113, 210–220. doi:10.1016/j.cmpb.2013.07.024Google Scholar
Taylor, Z. E., Eisenberg, N., & Spinrad, T. L. (2015). Respiratory sinus arrhythmia, effortful control, and parenting as predictors of children's sympathy across early childhood. Developmental Psychology, 51, 17–25. doi:10.1037/a0038189Google Scholar
Thayer, J. F., & Friedman, B. H. (1997). The heart of anxiety: A dynamical systems approach. In Vingerhoets, A. (Ed.), The (non) expression of emotions in health and disease (pp. 39–49). Amsterdam: Springer.Google Scholar
Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders, 61, 201–216. doi:10.1016/s0165-0327(00)00338-4Google Scholar
Thayer, J. F., & Lane, R. D. (2002). Perseverative thinking and health: Neurovisceral concomitants. Psychology and Health, 17, 685–695. doi:10.1080/08870440290025867Google Scholar
Thayer, J. F., & Sternberg, E. (2006). Beyond heart rate variability: Vagal regulation of allostatic systems. Annals of the New York Academy of Sciences, 1088, 361–372. doi:10.1196/annals.1366.014Google Scholar
Thelen, E., & Smith, L. B. (1996). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press.Google Scholar
Thompson, R. A., & Goodman, M. (2010). Development of emotion regulation. In King, A. M. & Sloan, D. M. (Eds.), Emotion regulation and psychopathology: A transdiagnostic approach to etiology and treatment (pp. 38–58). New York: Guilford Press.Google Scholar
Thompson, R. A., Lewis, M. D., & Calkins, S. D. (2008). Reassessing emotion regulation. Child Development Perspectives, 2, 124–131. doi:10.1111/j.1750-8606.2008.00054.xGoogle Scholar
Thompson, R. B., Cothran, T., & McCall, D. (2012). Gender and age effects interact in preschoolers' help-seeking: Evidence for differential responses to changes in task difficulty. Journal of Child Language, 39, 1107–1120. doi:10.1017/s030500091100047xGoogle Scholar
Tuzcu, V., Nas, S., Börklü, T., & Ugur, A. (2006). Decrease in the heart rate complexity prior to the onset of atrial fibrillation. Europace, 8, 398–402. doi:10.1093/europace/eul031Google Scholar
Valenza, G., Nardelli, M., Lanata, A., Gentili, C., Bertschy, G., & Scilingo, E. P. (2015). Predicting mood changes in bipolar disorder through heartbeat nonlinear dynamics: A preliminary study. In Proceedings of the 2015 Computing in Cardiology Conference (pp. 801–804). Nice, France: IEEE.Google Scholar
Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved three-step approaches. Political Analysis, 18, 450–469. doi:10.1093/pan/mpq025Google Scholar
Vikman, S., Mäkikallio, T. H., Yli-Mäyry, S., Pikkujämsä, S., Koivisto, A. M., Reinikainen, P., … Huikuri, H. V. (1999). Altered complexity and correlation properties of RR interval dynamics before the spontaneous onset of paroxysmal atrial fibrillation. Circulation, 100, 2079–2084. doi:10.1161/01.cir.100.20.2079Google Scholar
Wallot, S., & Kelty-Stephen, D. G. (2018). Interaction-dominant causation in mind and brain, and its implication for questions of generalization and replication. Minds and Machines, 28, 353–374. doi:10.1007/s11023-017-9455-0Google Scholar
Williford, A. P., Calkins, S. D., & Keane, S. P. (2007). Predicting change in parenting stress across early childhood: Child and maternal factors. Journal of Abnormal Child Psychology, 35, 251–263. doi:10.1007/s10802-006-9082-3Google Scholar
Yentes, J. M., Hunt, N., Schmid, K. K., Kaipust, J. P., McGrath, D., & Stergiou, N. (2013). The appropriate use of approximate entropy and sample entropy with short data sets. Annals of Biomedical Engineering, 41, 349–365. doi:10.1007/s10439-012-0668-3Google Scholar
Zahn, D., Adams, J., Krohn, J., Wenzel, M., Mann, C. G., Gomille, L. K., … Kubiak, T. (2016). Heart rate variability and self-control—A meta-analysis. Biological Psychology, 115, 9–26. doi:10.1016/j.biopsycho.2015.12.007Google Scholar